Dual-channel Quaternion Convolutional Network for Denoising

Abstract

The color image denoising based on deep learning usually uses convolution on each channel, and then merges multi-channel data into single channel data. This method does not fully consider the spectral correlation between color channels, which may casuse distortion of the denoising results. Quaternion convolution can solve this problem by treating a color pixel as a whole. However, a single quaternion convolutional network can not restore the image details well. To solve the problem, a dual-channel quaternion convolutional network (DQNet) for color random impulse noise removal is proposed. Firstly, according to the strategy of structure channel and color channel fusion, a structure detail restoration network based on dilated convolution is proposed to obtain structure and edge features, and quaternion convolution network is used to extract cross-channel color information. Secondly, aiming at the problem that convolution operation will cause partial global information loss, the long line connection is used to fuse the input noise image with the convolution results, and then, a feature enhancement module based on attention mechanism is designed to guide the network to extract potential noise features from complex background. Finally, the residual learning is used to achieve the restoration of color random impulse noise. Experimental results show that the proposed algorithm has better denoising performance, especially in moderate noise level or high noise level.

Keywords

• image denoising; deep learning; random impulsive noise; quaternion convolution; dual-channel